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Multi-Objective Non-Dominated Sorting Genetic Algorithm Optimization for Optimal Hybrid (Wind and Grid)-Hydrogen Energy System Modelling

Author

Listed:
  • Esmeralda Mukoni

    (Department of Electrical and Electronic Engineering, Stellenbosch University, Stellenbosch 7600, South Africa)

  • Karen S. Garner

    (Department of Electrical and Electronic Engineering, Stellenbosch University, Stellenbosch 7600, South Africa)

Abstract

In this paper, an optimal hybrid (wind and grid)-hydrogen energy system (H-HES) is proposed using multi-objective non-dominated sorting algorithm (NSGA-II) optimization. The H-HES consists of the main energy system; wind-energy system (W-ES), which supplies a proton exchange membrane (PEM) electrolyzer via an energy management system (EMS) and a rectifier. In addition, the grid-energy system (G-ES) is available to support the W-ES to meet the PEM electrolyzer’s energy demand, and the EMS facilitates control between the W-ES and G-ES. The W-ES is modelled using wind data from Wind Atlas South Africa (WASA) for six Renewable Energy Development Zones (REDZs) in South Africa and their appropriate wind turbine models. The selection of appropriate wind turbine models is guided by the optimal wind turbine variables obtained from NSGA-II corresponding to the optimal H-HES model. The optimal H-HES model is developed using two objective functions: cost of electricity and efficiency, which are minimized and maximized respectively and evaluated using NSGA-II available in Pymoo framework. NSGA-II successfully converges to a Pareto front, and the best solution for the H-HES cost of electricity and efficiency for each wind REDZ is determined by compromise programming; a multi-criteria decision-making technique available in Pymoo. From the optimal cost of electricity and efficiency solutions, optimal variables are successfully obtained for optimal modelling of the H-HES for each wind REDZ.

Suggested Citation

  • Esmeralda Mukoni & Karen S. Garner, 2022. "Multi-Objective Non-Dominated Sorting Genetic Algorithm Optimization for Optimal Hybrid (Wind and Grid)-Hydrogen Energy System Modelling," Energies, MDPI, vol. 15(19), pages 1-18, September.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:19:p:7079-:d:926010
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    References listed on IDEAS

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    1. Paweł Ziółkowski & Marta Drosińska-Komor & Jerzy Głuch & Łukasz Breńkacz, 2023. "Review of Methods for Diagnosing the Degradation Process in Power Units Cooperating with Renewable Energy Sources Using Artificial Intelligence," Energies, MDPI, vol. 16(17), pages 1-28, August.
    2. Yingming Liu & Shuyuan Zhang & Xiaodong Wang & Hongfang Xie & Tian Cao, 2022. "Optimization of Pitch Control Parameters for a Wind Turbine Based on Tower Active Damping Control," Energies, MDPI, vol. 15(22), pages 1-22, November.
    3. Isaac Amoussou & Emmanuel Tanyi & Ahmed Ali & Takele Ferede Agajie & Baseem Khan & Julien Brito Ballester & Wirnkar Basil Nsanyuy, 2023. "Optimal Modeling and Feasibility Analysis of Grid-Interfaced Solar PV/Wind/Pumped Hydro Energy Storage Based Hybrid System," Sustainability, MDPI, vol. 15(2), pages 1-30, January.

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